SoroushVahidi/scidocs huggingface dataset:
"If It Matters, I Can Explain It": Social Desirability of Knowledge Increases the Illusion of Explanatory Depth
(2017)

This paper explores whether social desirability affects the illusion of explanatory depth (IEOD) by comparing the magnitude of this illusion in topics with different levels of social desirability within several domains. This question was chosen because prior literature shows that social expectations about how much a person should know about a certain topic affect the magnitude of the IOED. Previous research shows also that social desirability has an effect on a similar illusion related to argumentation, and that the IOED is affected by the way a person thinks knowledge is distributed in his or her social group. In order to do so, 184 participants were assigned randomly to three knowledge domains (history, economics, and devices) and in each domain they rated their understanding of a high-desirability and a low-desirability topic following a standard IOED procedure. Results show that social desirability has an effect on the IOED magnitude and that overestimation of understanding varies among domains. Particularly, participants tend to overestimate their understanding of high desirability topics only. This effect was stronger in the historical domain.
Behavioral oscillation in priming: competing perceptual predictions conveyed in alternating theta-band rhythms.
(2015)

The brain constantly creates perceptual predictions about forthcoming stimuli to guide perception efficiently. Abundant studies have demonstrated that perceptual predictions modulate sensory activities depending on whether the actual inputs are consistent with one particular prediction. In real-life contexts, however, multiple and even conflicting predictions might concurrently exist to be tested, requiring a multiprediction coordination process. It remains largely unknown how multiple hypotheses are conveyed and harmonized to guide moment-by-moment perception. Based on recent findings revealing that multiple locations are sampled alternatively in various phase of attentional rhythms, we hypothesize that this oscillation-based temporal organization mechanism may also underlie the multiprediction coordination process. To address the issue, we used well established priming paradigms in combination with a time-resolved behavioral approach to investigate the fine temporal dynamics of the multiprediction harmonization course in human subjects. We first replicate classical priming effects in slowly developing trends of priming time courses. Second, after removing the typical priming patterns, we reveal a new theta-band (∼4 Hz) oscillatory component in the priming behavioral data regardless of whether the prime was masked. Third, we show that these theta-band priming oscillations triggered by congruent and incongruent primes are in an out-of-phase relationship. These findings suggest that perceptual predictions return to low-sensory areas not continuously but recurrently in a theta-band rhythm (every 200-300 ms) and that multiple predictions are dynamically coordinated in time by being conveyed in different phases of the theta-band oscillations to achieve dissociated but temporally organized neural representations.
The Impact of Experimental Setup in Prepaid Churn Prediction for Mobile Telecommunications: What to Predict, for Whom and Does the Customer Experience Matter?
(2010)

Prepaid customers in mobile telecommunications are not bound by a contract and can therefore change operators (‘churn’) at their convenience and without notification. This makes the task of predicting prepaid churn both challenging and financially rewarding. This paper presents an explorative, real world study of prepaid churn modeling by varying the experimental setup on three dimensions: data, outcome definition and population sample. Firstly, we add Customer Experience Management (CEM) data to data traditionally used for prepaid churn prediction. Secondly, we vary the outcome definition of prepaid churn. Thirdly, we alter the sample of the customers included, based on their inactivity period at the time of recording. While adding CEM parameters did not influence the predictability of churn, one variation on the sample and especially a particular change in the outcome definition had a substantial
A Comparative Study of Staff Removal Algorithms
(2008)

This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization-based approach. We define three different error metrics, compare the algorithms with respect to these metrics, and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation, our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal but applicable to other segmentation problems as well.
PUSHOVER ANALYSIS OF A MULTISTORIED BUILDING
(2016)

To model the advanced behaviour of reinforced concrete analytically in its non-linear zone is difficult. This has led engineers in the past to believe heavily on empirical formulas that were derived from numerous experiments for the design of reinforced concrete structures. For structural design and assessment of reinforced concrete members, the nonlinear analysis has become a vital tool. The method can be used to study the behaviour of reinforced concrete structures as well as force redistribution. This analysis of the nonlinear response of RC structures to be distributed out in an exceedingly routine fashion. It helps within the investigation of the behaviour of the structure below completely different loading conditions and also the cracks pattern. In the present study, the non-linear response of RCC frame using the loading has been carried out with the intention to investigate the relative importance of many factors in the non-linear analysis of RCC frames. The structure was modelled and analyzed in STAAD Pro V8i, SAP2000 and designed manually. The description of the reinforcement was done in AutoCAD 2010. Keywordsmulti-storied building, STAAD PRO, Floor plan, SAP 2000, Push over analysis.
Automated Irrigation System-IoT Based Approach
(2018)

Agriculture is a major source of earning of Indians and agriculture has made a big impact on India's economy. The development of crops for a better yield and quality deliver is exceptionally required. So suitable conditions and suitable moisture in beds of crop can play a major role for production.. Mostly irrigation is done by tradition methods of stream flows from one end to other. Such supply may leave varied moisture levels in filed. The administration of the water system can be enhanced utilizing programmed watering framework This paper proposes a programmed water system with framework for the terrains which will reduce manual labour and optimizing water usage increasing productivity of crops. For formulating the setup, Arduino kit is used with moisture sensor with Wi-Fi module. Our experimental setup is connected with cloud framework and data is acquisition is done. Then data is analysed by cloud services and appropriate recommendations are given.
Self-Autonomous Wireless Sensor Nodes With Wind Energy Harvesting for Remote Sensing of Wind-Driven Wildfire Spread
(2011)

The satellite-based remote sensing technique has been widely used in monitoring wildfire spread. There are two prominent drawbacks with this approach of using satellites located in space: (1) very low sampling rate (temporal resolution problem) and (2) lack of accuracy (spatial and spectral resolution problem). To address these challenges, a wireless sensor network deployed at ground level with high-fidelity and low-altitude atmospheric sensing for wind speed of local wildfire spread has been used. An indirect approach in sensing wind speed has been proposed in this paper as an alternative to the bulky conventional wind anemometer to save cost and space. The wind speed is sensed by measuring the equivalent electrical output voltage of the wind turbine generator (WTG). The percentage error in the wind speed measurement using the proposed indirect method is measured to be well within the ±4% limit with respect to wind anemometer accuracy. The same WTG also functions as a wind energy harvesting (WEH) system to convert the available wind energy into electrical energy to sustain the operation of the wireless sensor node. The experimental results show that the designed WEH system is able to harvest an average electrical power of 7.7 mW at an average wind speed of 3.62 m/s for powering the operation of the wireless sensor node that consumes 3.5 mW for predicting the wildfire spread. Based on the sensed wind speed information, the fire control management system determines the spreading condition of the wildfire, and an adequate fire suppression action can be performed by the fire-fighting experts.
Opposition-based Magnetic Optimization Algorithm with parameter adaptation strategy
(2016)

Magnetic Optimization Algorithm (MOA) has emerged as a promising optimization algorithm that is inspired by the principles of magnetic field theory. In this paper we improve the performance of the algorithm in two aspects. First an Opposition-Based Learning (OBL) approach is proposed for the algorithm which is applied to the movement operator of the algorithm. Second, by learning from the algorithm’s past experience, an adaptive parameter control strategy which dynamically sets the parameters of the algorithm during the optimization is proposed. To show the significance of the proposed parameter adaptation strategy, we compare the algorithm with two well-known parameter setting techniques on a number of benchmark problems. The results indicate that although the proposed algorithm with the adaptation strategy does not require to set the parameters of the algorithm prior to the optimization process, it outperforms MOA with other parameter setting strategies in most large-scale optimization problems. We also study the algorithm while employing the OBL by comparing it with the original version of MOA. Furthermore, the proposed algorithm is tested and compared with seven traditional population-based algorithms and eight state-of-theart optimization algorithms. The comparisons demonstrate that the proposed algorithm outperforms the traditional algorithms in most benchmark problems, and its results is comparative to those obtained by the state-of-the-art algorithms.
Lightweight IPS for port scan in OpenFlow SDN networks
(2018)

Security has been one of the major concerns for the computer network community due to resource abuse and malicious flows intrusion. Before a network or a system is attacked, a port scan is typically performed to discover vulnerabilities, like open ports, which may be used to access and control them. Several studies have addressed Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) methods for detecting malicious activities, based on received flows or packet data analysis. However, those methods lead to an increase in switching latency, due to the need to analyze flows or packets before routing them. This may also increase network overhead when flows or packets are duplicated to be parsed by an external IDS. On the one hand, an IDS/IPS may be a bottleneck on the network and may not be useful. On the other hand, the new paradigm called Software Defined Networking (SDN) and the OpenFlow protocol provide some statistical information about the network that may be used for detecting malicious activities. Hence, this work presents a new port scan IPS for SDN based on the OpenFlow switch counters data. A non-intrusive and lightweight method was developed and implemented, with low network overhead, and low memory and processing power consumption. The results showed that our method is effective on detecting and preventing port scan attacks.
Improved Option Pricing Using Artificial Neural Networks and Bootstrap Methods
(1997)

A hybrid neural network is used to predict the difference between the conventional option-pricing model and observed intraday option prices for stock index option futures. Confidence intervals derived with bootstrap methods are used in a trading strategy that only allows trades outside the estimated range of spurious model fits to be executed. Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods. A modified bootstrap predictor is indexed by a parameter that allows the predictor to range from a pure bootstrap predictor, to a hybrid predictor, and finally the bagging predictor. The modified bootstrap predictor outperforms the hybrid and bagging predictors. Greatly improved performance was observed on the boundary of the training set and where only sparse training data exists. Finally, bootstrap bias estimates were studied.
High quality & low thermal resistance eutectic flip chip LED bonding
(2013)

Die attach process plays a key role in the thermal management of high power LED packages by governing the thermal resistance of the solder interfaces between the LED chips and the substrate materials. In this paper, comparison was made between the GaN based flip chip LED packages fabricated by (1) heated collet and (2) flux reflow eutectic die attach process. Thermal transient characteristics of the samples had been investigated based on the evaluation of the differential structure function and chip temperature measurement by infrared microscopy. It was shown that LEDs with higher void content would result in higher thermal resistance and chip temperature. Under high power operation at 6W, it was estimated that the junction temperature would exceed 80°C with void content 40%, which was at least 10°C higher comparing to that with void content 20%. Based on the result, LEDs samples bonded by heated collet die attach process had an average void content of 8.8% with 0.9% standard deviation, which were smaller comparing to the samples bonded by flux reflow die attach process (40% void content with standard deviation 20.5%). Due to the advantage of small void content control, it is possible to fabricate high power LED package with low thermal resistance, consistent and reliable die attach quality by using heated collet die attach process.
Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models
(2011)

Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
(2017)

We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and nally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.
Multiple templates auto exposure control based on luminance histogram for onboard camera
(2011)

In order to adjust the exposure of high resolution and frequency images from an on-board camera in real time, a simple exposure control approach based on luminance histogram analysis is presented to realize light control precisely and rapidly. The algorithm divides the initial image into nine blocks and calculates the mean brightness of every part. It uses the luminance histogram to analyze the degree of exposure of both: the global image and every part, especially interest areas. According to the area size of the neighborhood on the two edges of the histogram, the different weight matrix can be decided to quickly calculate the optimal brightness for the next frame, and then the optimal exposure time can be determined accurately. The algorithm does not need to determine an expensive quadratic function to decide the weight of every part so as to fast perform auto exposure control. The experimental results show that the algorithm can correctly and fast adjust the exposure time of the on-board camera on an autonomous robot, and has strong adaptability for various shootings outdoor.
A Facial Expression Recognition System Using Convolutional Networks
(2015)

Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.
Analysis of user keyword similarity in online social networks
(2010)

How do two people become friends? What role does homophily play in bringing two people closer to help them forge friendship? Is the similarity between two friends different from the similarity between any two people? How does the similarity between a friend of a friend compare to similarity between direct friends? In this work, our goal is to answer these questions. We study the relationship between semantic similarity of user profile entries and the social network topology. A user profile in an on-line social network is characterized by its profile entries. The entries are termed as user keywords. We develop a model to relate keywords based on their semantic relationship and define similarity functions to quantify the similarity between a pair of users. First, we present a ‘forest model’ to categorize keywords across multiple categorization trees and define the notion of distance between keywords. Second, we use the keyword distance to define similarity functions between a pair of users. Third, we analyze a set of Facebook data according to the model to determine the effect of homophily in on-line social networks. Based on our evaluations, we conclude that direct friends are more similar than any other user pair. However, the more striking observation is that except for direct friends, similarities between users are approximately equal, irrespective of the topological distance between them.
Effects of energy drinks on the cardiovascular system
(2017)

Throughout the last decade, the use of energy drinks has been increasingly looked upon with caution as potentially dangerous due to their perceived strong concentration of caffeine aside from other substances such as taurine, guarana, and L-carnitine that are largely unknown to the general public. In addition, a large number of energy drink intoxications have been reported all over the world including cases of seizures and arrhythmias. In this paper, we focus on the effect of energy drinks on the cardiovascular system and whether the current ongoing call for the products' sales and regulation of their contents should continue.
Boosting item keyword search with spreading activation
(2005)

Most keyword search engines return directly matching keyword phrases. However, publishers cannot anticipate all possible ways in which users would search for the items in their documents. In fact, many times, there may be no direct keyword match between a keyword search phrase and descriptions of relevant items that are perfect matches for the search. We present an automated, high precision-based information retrieval solution to boost item find-ability by bridging the semantic gap between item information and popular keyword search phrases. Our solution achieves an average of 80% F-measure for various boosted matches for keyword search phrases in various categories.
Advanced Cooling for Power Electronics
(2012)

Power electronics devices such as MOSFET's, GTO's, IGBT's, IGCT's etc. are now widely used to efficiently deliver electrical power in home electronics, industrial drives, telecommunication, transport, electric grid and numerous other applications. This paper discusses cooling technologies that have evolved in step to remove increasing levels of heat dissipation and manage junction temperatures to achieve goals for efficiency, cost, and reliability. Cooling technologies rely on heat spreading and convection. In applications that use natural or forced air cooling, water heat pipes provide efficient heat spreading for size and weight reduction. Previous concepts are reviewed and an improved heat sink concept with staggered fin density is described for more isothermal cooling. Where gravity can drive liquid flow, thermosiphons provide efficient heat transport to remote fin volumes that can be oriented for natural and/or forced air cooling. Liquid cold plates (LCP's) offer the means to cool high heat loads and heat fluxes including double sided cooling for the highest density packaging. LCP's can be used both in single phase cooling systems with aqueous or oil based coolants and in two-phase cooling systems with dielectric fluids and refrigerants. Previous concepts are reviewed and new concepts including an air cooled heat sink, a thermosiphon heat sink, a vortex flow LCP and a shear flow direct contact cooling concept are described.